Semi-supervised domain adaptation (SSDA) is an essential task that bridges the gap between a well-organized source domain and a target domain, thus promoting distribution consistency of features between the two domains using a small set of labeled data. Recent semi-supervised domain adaptation methods have shown remarkable progress for image classification [1]}, [2]}, [3]}, [4]}. Kim et al. [1]} treat SSDA as two separate problems: UDA and SSL. They argue that conventional domain adaptation and semi-supervised learning methods result in less effective or negative transfer in SSDA, and so they propose to align features by alleviating intra-domain discrepancy. This is done by adopting co-training [6]} to exchange the expertise between two classifiers which are trained on mixed data via a technique termed MixUp [7]} to mix labeled and unlabeled data in the target domain. The proposed approach addresses a similar issue in semantic segmentation.
